Methods of spatial normalization of positron emission tomography images

ABSTRACT

An adaptive template image for registering a PET or a SPECT image includes a template image model including variability of values for each voxel in a template image according to one or more control parameters.

This application is a filing under 35 U.S.C. 371 of internationalapplication number PCT/US2012/056182, filed Sep. 20, 2012, which claimspriority to U.S. application No. 61/536,702 filed Sep. 20, 2011, theentire disclosure of which is hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of positron emissiontomography (PET) image analysis and single photon emission tomography(SPECT). More specifically, the present invention relates to a method ofspatial normalization of PET and SPECT images.

BACKGROUND OF THE INVENTION

When making automated inter-individual quantitative analysis from PETand SPECT imaging data it is important to have a robust and accuratespatial normalization method, which can be used to transform images fromdifferent individuals into a common reference space where comparisonsbetween individuals can be made. This means that different organ images,for example brain images, are transformed into a standard anatomicalspace in which the organs from different individuals have the sameposition, size and shape, so as to allow for comparison across differentindividuals. This is relevant for both volume-of-interest (VOI) analysisas well as voxel-based analysis from imaging data.

For spatial normalization of PET and SPECT images, either the individualPET/SPECT image or a co-registered anatomical image may be used to findthe spatial transformation between an individual image and a templateimage located in the reference space. In most applications, the use ofanatomical information will enhance the possibility of making a goodspatial normalization. However, there is a trade-off in making a methoddependent on an anatomical image. For instance when there is noanatomical image available, the options would either be to disallow theanalysis completely, or to use an alternative PET/SPECT-based methodwhich would produce different results.

There is therefore a need in the art for a spatial normalization methodspecifically for PET and SPECT images, such as images using amyloidimaging agents, which only depends on the PET or SPECT image itself.

SUMMARY OF THE INVENTION

In view of the needs of the prior art, the present invention provides amodel-controlled adaptive template image integrated with a spatialnormalization method. The present invention may be applied to imagingagents such as, by way of illustration and not of limitation, amyloidimaging agents.

In one embodiment, the present invention provides an adaptive templateimage for registering a PET image, the adaptive template image includesa template image model wherein the values for each voxel in a templateimage vary (ie, have a variability) according to one or more controlparameters.

In another embodiment, the present invention is tuned to the specificproblem of registering imaging data from patients with Alzheimer'sDisease (AD), MCI, as well as individuals expressing a normal uptakepattern, a high uptake pattern, or a low uptake pattern.

In one embodiment, the variability of values for each voxel rangesbetween a value corresponding to the normal level of uptake of animaging agent and an abnormal level of uptake of the imaging agent.

For example, the level of uptake of the imaging agent may be indicate/bedetected for the grey matter of the brain. The abnormal level of uptakemay result from a relatively high level of uptake or a relatively lowlevel of uptake of the imaging agent.

In another embodiment, the variability of values for each voxel rangesbetween a value corresponding to a normal level of amyloid in greymatter and a high amyloid level in grey matter. It is furthercontemplated that the imaging agent may be [¹⁸F]Flutemetamol(Flutemetamol), where high levels of uptake in gray matter areindicative of high levels of amyloid. In is also contemplated that theimaging agent could be DaTSCAN®, sold by GE Healthcare of Amersham,U.K., where low levels of uptake in the striatum are indicative of lowlevels of dopamine transport.

In another embodiment, the present invention provides a method ofregistering a PET or SPECT image to an adaptive template imagecomprising the steps of:

-   -   Spatially normalizing an individual PET image to an adaptive        template image located in the reference space;    -   Comparing the spatially normalized individual PET image to the        adaptive template image to determine whether both are        sufficiently converged; and    -   Altering the parameter controlling the template image and the        parameters controlling the spatial transformation for the        individual PET image so as render an altered template image and        an updated spatially transformed individual PET image in the        event that the comparing step does not determine that the        spatially normalized individual PET image is sufficiently        converged to the adaptive template image.

In a further embodiment, the present invention provides a method ofconstructing an adaptive template comprising the steps of:

Calculating a mean image from a set of input PET/SPECT images showinguptake of an imaging agent in a region of interest, the set of inputPET/SPECT images having been transformed into a reference space; and

Calculating a regression model for each voxel in a set of images havingbeen transformed into a reference space, where the dependent variablerepresents the voxel intensities in the input images and is regressed onsome variable which for each input image represents a “true” value ofwhere on a scale from a normal level of uptake to a high level of uptakeof the imaging agent the voxel is located.

The present invention further provides non-transitory computer readablestorage medium comprising computer readable program code includinginstructions for registering a PET image to an adaptive template image,wherein execution of the computer readable program code causes aprocessor to carry out the steps of the method of registering a PETimage to an adaptive template image of the present invention.

The present invention still further provides a non-transitory computerreadable storage medium comprising computer readable program codeincluding instructions for using an adaptive template the presentinvention.

The present invention even further provides A system for registering aPET image to an adaptive template comprising:

means for transforming a moving image using selected start parameters;

a source for a template model;

means for modifying the template model using a control parameter so asto provide an adaptive template;

means for comparing the transformed moving image with the adaptivetemplate;

means for selecting new parameters to apply to the transformed image anda new control parameter for the adaptive template;

means for transforming the transformed moving image using the newparameters;

means for adjusting the adaptive template using the new template controlparameter; and

means for storing the transformed moving image when it has sufficientlyconverged with the adaptive template.

The present invention even still further provides a positron emissiontomography (PET) system comprising:

a storage device;

a detector for detecting positron emissions from a brain of a subject,wherein the detector generates signals representing the positronemissions that are stored in the storage device;

an image processor for, wherein the image processor is programmed to:

Spatially normalize an individual PET image to an adaptive templateimage located in the reference space;

Compare the spatially normalized individual PET image to the adaptivetemplate image to determine whether both are sufficiently converged; and

Alter the parameter controlling the template image and the parameterscontrolling the spatial transformation for the individual PET image soas render an altered template image and an updated spatially transformedindividual PET image in the event that the comparing step does notdetermine that the spatially normalized individual PET image issufficiently converged to the adaptive template image.

This system is further contemplated to include a display for displayingan image of the brain based on the SSP data set.

The present invention even yet further provides a positron emissiontomography (PET) system comprising:

an image processor for registering a PET image to an adaptive templateimage that is programmed to:

Spatially normalize an individual PET image to an adaptive templateimage located in the reference space;

Compare the spatially normalized individual PET image to the adaptivetemplate image to determine whether both are sufficiently converged; and

Alter the parameter controlling the template image and the parameterscontrolling the spatial transformation for the individual PET image soas render an altered template image and an updated spatially transformedindividual PET image in the event that the comparing step does notdetermine that the spatially normalized individual PET image issufficiently converged to the adaptive template image.

The present invention even still yet further provides acomputer-implemented method of registering a registering a PET image toan adaptive template image, the method comprising:

Spatially normalizing an individual PET image to an adaptive templateimage located in the reference space;

Comparing the spatially normalized individual PET image to the adaptivetemplate image to determine whether both are sufficiently converged; and

Altering the parameter controlling the template image and the parameterscontrolling the spatial transformation for the individual PET image soas render an altered template image and an updated spatially transformedindividual PET image in the event that the comparing step does notdetermine that the spatially normalized individual PET image issufficiently converged to the adaptive template image.

The present invention also still further provides a computer-implementedmethod of constructing an adaptive template comprising the steps of:

Calculating a mean image from a set of input PET images showing uptakeof an imaging agent in a region of interest, the set of input PET imageshaving been transformed into a reference space; and

Calculating a regression model for each voxel in the set of imageshaving been transformed into a reference space, where the dependentvariable represents the voxel intensities in the input images and isregressed on some variable which for each input image represents a“true” value of where on a scale from a normal level of uptake to a highlevel of uptake of the imaging agent the voxel is located.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides an illustration of typical Flutemetamol uptake patternsin an amyloid positive (Aβ+) and amyloid negative (Aβ−) scan.

FIG. 2 depicts an outline of the complete image registration procedureincluding the novel extension.

FIG. 3 depicts an illustration of the calculated mean SUVR values of thecomposite region COM_(SUVR) for all subjects in two different groups ofAβ− and Aβ+ scans.

FIG. 4 depicts the resulting slope and intercept images according to oneembodiment of the present invention.

FIG. 5 shows template images of the present invention.

FIG. 6 depicts a system for performing the instant invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention provides a spatial normalization method when usingimaging agents. While a specific embodiment for scans with the[¹⁸F]-Flutemetamol amyloid imaging agent, which only depends on theFlutemetamol scan itself, are described in detail, the present inventionis also applicable to other imaging agents used for other imagingapplications. For example, the present invention may also be employedfor DaTSCAN imaging where low uptake in the brain is indicative ofdisease.

The uptake pattern in Flutemetamol scans can differ much between normalsubjects (Aβ−) and subjects with high amyloid in gray matter (Aβ+).Therefore the spatial normalization method has been extended with anadaptive template mechanism, which during the image registration processalters the template image along with the spatial transformationparameters to make it more similar to the scan being registered. Byusing this extension, the method can accurately spatially normalizescans ranging over the whole amyloid scale from Aβ− to very high Aβ+.

The present invention solves this by creating a model over how thetypical uptake pattern in the whole image varies along the scale goingfrom Aβ− to Aβ+. In the model presented hereinbelow, a one parameterlinear regression model for each voxel in the image is used to expressthe dependence, but generally a more advanced model expressing uptakepattern dependence on multiple parameters, is also contemplated by thepresent invention. The model is built once for all in a separatepre-processing step and is then fixed as an internal part of the method.Although the method described below has been developed for Flutemetamolit should be noted that other PET amyloid tracers exhibit exactly thesame characteristic. Moreover, the method described is general and thereis nothing limiting the method of the present invention to PET amyloidscans only. As long as a model over the typical variation modes of theimages being registered can be created, the adaptive template method haspotential to be used for other imaging agents as well as for both PETand SPECT applications. While the examples provided hereinbelow aredirected PET applications, the present invention also applies to SPECTapplications.

As noted hereinabove, the present invention may alter one or more of theparameters for the image, the image template, or both. For example,parameters for spatial transformation may include translation along eachaxis (providing three possible parameters), scaling along each axis(providing another three possible parameters), and rotation about eachaxis (providing an additional three parameters), for a total of up tonine parameters which may be altered by the present invention.Additionally, parameters which account for shape differences between theimage and the adaptive template provide additional parameters which maybe altered by the present invention An illustration of typicalFlutemetamol uptake patterns in an Aβ+ and Aβ− scan is shown in FIG. 1.It can be noted that the Aβ+ have much more activity in cortical graymatter and even though white matter activity is about the same in thetwo cases the relative activity pattern between white and gray matter isto some extent reversed. That is from having highest activity in whitematter in Aβ− to the opposite with highest, or at least as high,activity in gray matter in Aβ+.

Spatial normalization is the process of transforming a scan from patientspace to a standard space thus allowing for comparison of data fromdifferent subjects. Typically this is performed through an iterativeprocess where the patient scan is compared with a template image andwhere it is geometrically transformed to be as similar to the templateas possible. However, by observing the patterns above it can be seenthat there is no obvious way of selecting a similarity metric for aspatial normalization method which would be able to perform accurate androbust registrations of both of these types of images, by comparing themto a common template image.

Therefore, the present invention provides an extension to be usedtogether with a standard image registration method, where the number ofpossible template images is infinite. Basically, no definite selectionof one specific template image occurs. Instead the intensities in thetemplate are continuously altered during the registration, to make thetemplate converge along with the spatial transformation parameters, tooptimally fit the image being registered. An outline of the completeimage registration procedure including the novel extension is shown inFIG. 2.

First, the step of providing a template model 100 is performed. Step 100is a pre-processing step performed once during a design phase to preparethe adaptive template of the present invention. Separately, a movingimage (ie, the patient image) 110, such as a PET or SPECT scan, is takenof a subject and the step of choosing starting parameters 120 for themoving image is performed. Including within step 120 is the step ofchoosing a template control start parameter. In short the followingsteps are then iterated until convergence is reached. An adaptivetemplate is then built 130 using the control parameter for the adaptivetemplate and the model from step 100. A transforming step 140 isperformed in which the moving image is adjusted using the startparameters. Then, a comparison step 150 is performed which compares thesimilarity between the transformed moving image and the adaptivetemplate. In this step, the similarity metric is evaluated from thevoxel values in the template image and the transformed moving image. Ifthe moving image and the adaptive template are found to be sufficientlyor maximally converged, the registration of the moving image iscompleted, step 160. Should the moving image and the adaptive templatenot be sufficiently or maximally converged, the step of choosing newparameters 170, for both the moving image and the template controlparameter, is performed. Then transforming step 140 is repeated, thistime using the parameters chosen during step 170. Additionally, step 130is repeated in which the existing adaptive template is also modified bythe new template control parameter chosen during step 170. Thecomparison step 150 is again performed using the newly transformedmoving image and adaptive temple. If the method has not converged,another set of adaptive template and transformation parameters isselected by the optimization method and a new evaluation of thesimilarity metric is made. The present invention contemplates that theoptimization method may be any numerical optimization method.

Convergence may be sufficiently achieved when the value of a functionfor describing the similarity between the images has reached a leveldeemed suitable for proceeding. Convergence is desirably achieved whenthe value of a function for describing the similarity between the imageshas reached its maximum, ie, showing maximum similarity between thetemplate image in reference space and the transformed image. Thesimilarity metric could be based, for example, on correlation or onmutual information, although the present invention contemplates that anysuitable similarity metric may be applied.

The pre-processing step 100 is performed once and the extra parameter orparameters controlling the template model are added to the registrationparameters and altered by the optimization method in a similar way asthe parameters controlling the geometry change. The following will nowin detail describe the creation of a one-parameter linear templatemodel, which has been found to be suitable for Flutemetamol PET scans.

The present invention is contemplated to provide a non-transitorycomputer-readable storage medium with an executable program forperforming the method of the present invention. This computer-readablestorage medium includes computer-readable program code includesinstructions for performing a method of the present invention, such thatexecution of the computer-readable program code causes a processor toperform the steps of the method of the present invention. Additionally,the present invention is contemplated to use an adaptive template modelof the present invention for performing the spatial normalization methodof the present invention. The starting parameters may be fixed for allprocedures and the updated parameters may be selected automatically bythe process.

Adaptive Template Image Model

In the following example, Montreal Neurological Institute (MNI)reference space sampled to a uniform 2×2×2 mm voxel size is used asstandard space for the template image. A set of Flutemetamol inputimages is provided to create a template of the present invention, whichimages have already have been spatially normalized to this space usingan MR-based spatial normalization method.

Normally when creating a template image for spatial normalization onetakes these spatially-normalized input images and calculates a meanimage from these. It follows that this mean image will also be locatedin the reference space and can be used for spatial normalization of newunseen images by using an image registration method to find thetransformation making the new images match the template mean image.

However, as described hereinabove, the problem with this approach isthat the mean image will only be representative for one typical imagepattern for a specific level of amyloid, i.e. either the pattern of aAβ−, Aβ+, or something in between. To be able to create a model of theimage pattern variability the present invention instead calculates aregression model for each voxel in the template image, where thedependent variable which is the voxel intensities in the input images isregressed on some variable which ideally for each input image expressesa “true” value of where on a scale from Aβ− to very severe Aβ+ it islocated.

In practice, it is impossible to extract such true values but, beingprincipally interested in finding a robust estimated value for each scanon a one-dimensional scale of how much Aβ the gray matter contains, thisapproach provides such a robust estimate. Therefore the presentinvention calculates for all input images a mean standard uptake ratiovalue (SUVR) inside a large composite region (COM) covering regionsknown to be most affected by Aβ in Alzheimer's disease patients. Theseregions were the prefrontal cortex, parietal cortex, precuneus, lateraltemporal cortex, and the anterior and posterior cingulate cortices.

An illustration of the calculated SUVR values of the composite regionCOM_(SUVR) for all subjects in two different groups of Aβ− and Aβ+ scansis shown in FIG. 3.

It is thus seen that the present invention uses a good sampling over thewhole spectrum from Aβ− subjects with very low Flutemetamol uptakeinside COM to Aβ+ subjects with a very high Flutemetamol uptake.

In the next step, the linear regression model was applied for each voxeli in the template image matrix to express the dependence of thatparticular voxel, y_(i), on the COM_(SUVR) values according to Equation1:y _(i)={circumflex over (α)}_(i)+{circumflex over (β)}_(i) COM_(SUVR)+ε_(i)  Equation 1

-   -   Linear regression model        Applying this model over all input images regressed on the        corresponding COM_(SUVR) values provides an intercept image and        a slope image according to Equation 2:        I ₀=[{circumflex over (α)}₁ {circumflex over (α)}₂ . . .        {circumflex over (α)}_(n)]        I _(Slope)=[{circumflex over (β)}₁ β₂ . . . {circumflex over        (β)}_(n)]  Equation 2    -   Intercept image, I₀, and slope image, I_(Slope), created from        linear regression, where n is the number of voxels in the        images.

The intercept image will correspond to a pattern of a fully normalsubject located in the lower part of the COM_(SUVR) scale. The slopeimage, on the other hand, will have the highest values for the parts ofthe image where the largest changes occur when going from low to high onthe COM_(SUVR) scale. These resulting images are shown in FIG. 4.

From these images the present invention creates synthetic simulatedimages, along the whole COM_(SUVR) scale over the range from Aβ− to Aβ+.This is done by adding multiplicatively scaled portions of I_(Slope) toI₀ according to Equation 3:I _(template) =I ₀ +I _(slope x)  Equation 3

-   -   Template image equation        A value of the scale factor x of 0.0 will correspond to I₀        itself, which represents a pattern of an Aβ− subject, and a        value of 1.0 will correspond to an Aβ+ subject in the high end        of the COM_(SUVR) scale. From this formula the present invention        can create synthetic images for any x along the linear path        defined by I_(Slope). FIG. 5 shows such synthetic images        covering values of x from 0.0 to 1.0 in steps of 0.2.

Such synthetic images are used as the adaptive template in a spatialnormalization method. The slope image, I_(Slope), and intercept image,I₀, is the adaptive template model is controlled by one single parameterx. This parameter x is then adjusted by the image registration algorithmin a similar way as any of the other parameters, and the template imageis updated accordingly any time the x parameter is changed.

Referring now to FIG. 6, the method of the present invention iscontemplated to be performed by system 200. System 200 includes ascanner 210 for performing PET or SPECT scans, a computer 220 forreceiving scan images, e.g, the patient image (aka, the moving image),and a database 230 for providing the adaptive template. Computer 220typically includes a display 222, an input device 224 such as a keyboard224 a and a mouse 224 b, and a processor 226. Processor 226 typicallyincludes software for performing the method of the instant inventionusing the adaptive template from database 230 and the moving image fromscanner 210. Additionally, processor 226 is contemplated to includenon-transitory computer readable storage medium with an executableprogram for using an adaptive template of the present invention and forregistering a PET image to an adaptive template image according to amethod of the instant invention using the moving image from scanner 210.The non-transitory computer readable storage medium includescomputer-readable program code including instructions for using anadaptive template of the present invention and for registering a PETimage to an adaptive template image. Connections between scanner 210,computer 220, and database 230 are contemplated to be by any means knownto the art, such as hardwire, wireless, or any combination thereof.Additionally, the present invention contemplates that processor 226 anddatabase 230 are connected such that processor 226 may return theupgraded adaptive template to database 230 upon completion of the methodof the present invention.

While the particular embodiment of the present invention has been shownand described, it will be obvious to those skilled in the art thatchanges and modifications may be made without departing from theteachings of the invention. The matter set forth in the foregoingdescription and accompanying drawings is offered by way of illustrationonly and not as a limitation. The actual scope of the invention isintended to be defined in the following claims when viewed in theirproper perspective based on the prior art.

What is claimed is:
 1. An adaptive template image system configured toperform spatial normalization and registration of a PET or a SPECT imageusing a single image registration procedure, the system comprising: astorage configured to store and provide an adaptive template imagecomprising a template image model including a variability of values forone or more voxels in the adaptive template image, wherein thevariability of values for each voxel ranges from a value correspondingto a normal level of uptake of an imaging agent to an abnormal level ofuptake of the imaging agent, and wherein the imaging agent includes adopamine transport agent DaTSCAN and a level of uptake is determined fora striatum, the variability of values adjustable using a controlparameter x, wherein the values of the voxels are defined by anequation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure;and an image processor configured to at least: initiate a firstregistration of the PET or SPECT image into the reference space;spatially normalize the PET or SPECT image to the adaptive templateimage in the first registration by: (i) adjusting the template imagemodel using a first control parameter, the adjusting to includeadjusting among the variability of values for one or more voxels in theadaptive template image based on the first control parameter, (ii)rendering the adaptive template image from the template image model,(iii) performing a spatial normalization operation by adjusting the PETor SPECT image using spatial transformation parameters, (iv) evaluatingthe adaptive template image and the PET or SPECT imaging to determine aconvergence of the adaptive template image to the PET or SPECT image,wherein the convergence is determined by comparing voxels of theadaptive template image to corresponding voxels of the PET or SPECTimage, and wherein image registration between the adaptive templateimage and the PET or SPECT image is not performed until the adaptivetemplate image converges to fit the PET or SPECT image, and (v) when theadaptive template image does not converge to fit the PET or SPECT image,continuously and adaptively altering both the adaptive template imageand the PET or SPECT image by selecting an updated control parameter torender the adaptive template image and updated spatial transformationparameters to spatially normalize the PET or SPECT image forre-evaluation at (iv) within the single registration procedure andwithout performing the image registration until the adaptive templateimage converges to fit the PET or SPECT image; and when the adaptivetemplate image converges to fit the PET or SPECT image, register the PETor SPECT image in the reference space to complete the firstregistration.
 2. The adaptive template image system of claim 1, whereinthe level of uptake is measured in gray matter of a brain.
 3. Theadaptive template image system of claim 1, wherein the variability ofvalues for each voxel ranges from a value corresponding to a normallevel of amyloid in grey matter to a raised amyloid level in greymatter.
 4. The adaptive template image system of claim 1, wherein thecontrol parameter is selected as part of a spatial normalizationoptimization process.
 5. The adaptive template image system of claim 1,wherein I₀ and I_(slope) are determined using regression analysis ofmultiple images spanning different levels of disease state.
 6. A methodof performing spatial normalization of a PET or a SPECT image, using asingle image registration procedure, and of registering the PET or theSPECT image to an adaptive template image comprising a template imagemodel comprising variability of values for one or more voxels in theadaptive template image, wherein the variability of values for eachvoxel ranges from a value corresponding to a normal level of uptake ofan imaging agent to an abnormal level of uptake of the imaging agent,and wherein the imaging agent includes a dopamine transport agentDaTSCAN and a level of uptake is determined for a striatum, thevariability of values adjustable using a control parameter x, whereinthe values of the voxels are defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure,the method comprising: initiating a first registration of the PET orSPECT image into the reference space; spatially normalizing the PET orSPECT image to the adaptive template image in the first registration by:adjusting the template image model using a first control parameter, theadjusting to include adjusting among the variability of values for oneor more voxels in the adaptive template image based on the first controlparameter; rendering the adaptive template image from the template imagemodel; performing a spatial normalization operation by adjusting the PETor SPECT image using spatial transformation parameters; evaluating theadaptive template image and the PET or SPECT imaging to determine aconvergence of the adaptive template image to the PET or SPECT image,wherein the convergence is determined by comparing voxels of theadaptive template image to corresponding voxels of the PET or SPECTimage, and wherein image registration between the adaptive templateimage and the PET or SPECT image is not performed until the adaptivetemplate image converges to fit the PET or SPECT image, and when theadaptive template image does not converge to fit the PET or SPECT image,continuously and adaptively altering both the adaptive template imageand the PET or SPECT image by selecting an updated control parameter torender the adaptive template image and updated spatial transformationparameters to spatially normalize the PET or SPECT image forre-evaluation within the single registration procedure and withoutperforming the image registration until the adaptive template imageconverges to fit the PET or SPECT image; and when the adaptive templateimage converges to fit the PET or SPECT image, registering the PET orSPECT image in the reference space to complete the first registration.7. The method of claim 6, wherein said spatial normalization furthercomprises performing linear and non-linear transformations on the PET orSPECT image to find a match between the template image model and the PETor SPECT image, and wherein said linear transformation comprising atleast one of translation, rotation, and scaling.
 8. A system forperforming spatial normalization of a PET or a SPECT image using asingle image registration procedure, and for registering the PET or theSPECT image to an adaptive template image comprising a template imagemodel including a variability of values for one or more voxels in theadaptive template image, wherein the variability of values for eachvoxel ranges from a value corresponding to a normal level of uptake ofan imaging agent to an abnormal level of uptake of the imaging agent,and wherein the imaging agent includes a dopamine transport agentDaTSCAN and a level of uptake is determined for a striatum, thevariability of values adjustable using a control parameter x, whereinthe values of the voxels are defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure,the system configured to: during a first registration of the PET orSPECT image into the reference space, spatially normalize the PET orSPECT image to the adaptive template image in the first registration by:adjusting the template image model using a first control parameter, theadjusting to include adjusting among the variability of values for oneor more voxels in the adaptive template image based on the first controlparameter, rendering the adaptive template image from the template imagemodel, performing a spatial normalization operation by adjusting the PETor SPECT image using spatial transformation parameters, evaluating theadaptive template image and the PET or SPECT imaging to determine aconvergence of the adaptive template image to the PET or SPECT image,wherein the convergence is determined by comparing voxels of theadaptive template image to corresponding voxels of the PET or SPECTimage, and wherein image registration between the adaptive templateimage and the PET or SPECT image is not performed until the adaptivetemplate image converges to fit the PET or SPECT image, and when theadaptive template image does not converge to fit the PET or SPECT image,continuously and adaptively altering both the adaptive template imageand the PET or SPECT image by selecting an updated control parameter torender the adaptive template image and updated spatial transformationparameters to spatially normalize the PET or SPECT image forre-evaluation within the single registration procedure and withoutperforming the image registration until the adaptive template imageconverges to fit the PET or SPECT image; and when the adaptive templateimage converges to fit the PET or SPECT image, register the PET or SPECTimage in the reference space to complete the first registration.
 9. Anon-transitory computer readable storage medium comprising computerreadable program code including instructions for performing spatialnormalization of a PET or a SPECT image, using a single imageregistration procedure, and for registering the PET or the SPECT imageto an adaptive template image comprising a template image modelcomprising variability of values for one or more voxels in the adaptivetemplate image, wherein the variability of values for each voxel rangesfrom a value corresponding to a normal level of uptake of an imagingagent to an abnormal level of uptake of the imaging agent, and whereinthe imaging agent includes a dopamine transport agent DaTSCAN and alevel of uptake is determined for a striatum, the variability of valuesadjustable using a control parameter x, wherein the values of the voxelsare defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure,wherein execution of the computer readable program code causes aprocessor to carry out a method comprising: initiating a firstregistration of the PET or SPECT image into the reference space;spatially normalizing the PET or SPECT image to the adaptive templateimage in the first registration by: adjusting the template image modelusing a first control parameter, the adjusting to include adjustingamong the variability of values for one or more voxels in the adaptivetemplate image based on the first control parameter; rendering theadaptive template image from the template image model; performing aspatial normalization operation by adjusting the PET or SPECT imageusing spatial transformation parameters; evaluating the adaptivetemplate image and the PET or SPECT imaging to determine a convergenceof the adaptive template image to the PET or SPECT image, wherein theconvergence is determined by comparing voxels of the adaptive templateimage to corresponding voxels of the PET or SPECT image, and whereinimage registration between the adaptive template image and the PET orSPECT image is not performed until the adaptive template image convergesto fit the PET or SPECT image, and when the adaptive template image doesnot converge to fit the PET or SPECT image, continuously and adaptivelyaltering both the adaptive template image and the PET or SPECT image byselecting an updated control parameter to render the adaptive templateimage and updated spatial transformation parameters to spatiallynormalize the PET or SPECT image for re-evaluation within the singleregistration procedure and without performing the image registrationuntil the adaptive template image converges to fit the PET or SPECTimage; and when the adaptive template image converges to fit the PET orSPECT image, registering the PET or SPECT image in the reference spaceto complete the first registration.
 10. A method of constructing anadaptive template image comprising a template image model including avariability of values for one or more voxels in the adaptive templateimage, the method comprising: transforming a set of PET or SPECT imagesinto a reference space using spatial transformation parameters;calculating a regression model for each voxel in the set of PET or SPECTimages having been transformed into the reference space, where adependent variable represents voxel intensities in the set of PET orSPECT images and is regressed on some variable which for each input PETor SPECT image represents a “true” value of where on a scale from anormal level of uptake to a high level of uptake of an imaging agent thevoxel is located; and generating, based on the template image model anda control parameter, the adaptive template image for registering, in asingle registration procedure, the PET or SPECT image, wherein the PETor SPECT image is to be registered by at least: spatially normalizing,in a first registration of the PET or SPECT image into the referencespace, the PET or SPECT image to the adaptive template image in thefirst registration by: adjusting the template image model using a firstcontrol parameter x, the adjusting to include adjusting among thevariability of values for one or more voxels in the adaptive templateimage based on the first control parameter x, wherein the variability ofvalues for each voxel ranges from a value corresponding to a normallevel of uptake of an imaging agent to an abnormal level of uptake ofthe imaging agent, and wherein the imaging agent includes amyloidimaging agent and a level of uptake is determined for a striatum,wherein the values of the voxels are defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the first control parameter x, rendering the adaptive template imagefrom the adjusted template image model, performing a spatialnormalization operation by adjusting the PET or SPECT image usingspatial transformation parameters, evaluating the adaptive templateimage and the PET or SPECT image to determine a convergence of theadaptive template image to the PET or SPECT image, wherein theconvergence is determined by comparing voxels of the adaptive templateimage to corresponding voxels of the PET or SPECT image, and whereinimage registration between the adaptive template image and the PET orSPECT image is not performed until the adaptive template image convergesto fit the PET or SPECT image, and when the adaptive template image doesnot converge to fit the PET or SPECT image, continuously and adaptivelyaltering both the adaptive template image and the PET or SPECT image byselecting an updated control parameter to render the adaptive templateimage and updated spatial transformation parameters to spatiallynormalize the PET or SPECT image for re-evaluation within the singleregistration procedure and without performing the image registrationuntil the adaptive template image converges to fit the PET or SPECTimage; and when the adaptive template image converges to fit the PET orSPECT image, registering the PET or SPECT image in the reference spaceto complete the first registration.
 11. The method of claim 10, whereinthe normal level of uptake corresponds to Aß− and the high level ofuptake corresponds to Aß+.
 12. The method of claim 10, wherein the setof PET or SPECT images have been transformed into the reference spacewith a co-registered anatomical image.
 13. A positron emissiontomography (PET) system comprising: a storage device configured to storeand provide an adaptive template image comprising a template image modelincluding a variability of values for one or more voxels in the adaptivetemplate image, wherein the variability of values for each voxel rangesfrom a value corresponding to a normal level of uptake of an imagingagent to an abnormal level of uptake of the imaging agent, and whereinthe imaging agent includes a dopamine transport agent DaTSCAN and alevel of uptake is determined for a striatum, the variability of valuesadjustable using a control parameter x, wherein the values of the voxelsare defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering a PET or a SPECT image inthe reference space during single image registration procedure; adetector for detecting positron emissions from a brain of a subject,wherein the detector generates signals representing the positronemissions that are stored in the storage device; an image processorconfigured to at least: initiate a first registration of the PET orSPECT image into the reference space; spatially normalize the PET orSPECT image to the adaptive template image in the first registration by:(i) adjusting the template image model using a first control parameter,the adjusting to include adjusting among the variability of values forone or more voxels in the adaptive template image based on the firstcontrol parameter, (ii) rendering the adaptive template image from thetemplate image model, (iii) performing a spatial normalization operationby adjusting the PET or SPECT image using spatial transformationparameters, (iv) evaluating the adaptive template image and the PET orSPECT imaging to determine a convergence of the adaptive template imageto the PET or SPECT image, wherein the convergence is determined bycomparing voxels of the adaptive template image to corresponding voxelsof the PET or SPECT image, and wherein image registration between theadaptive template image and the PET or SPECT image is not performeduntil the adaptive template image converges to fit the PET or SPECTimage, and (v) when the adaptive template image does not converge to fitthe PET or SPECT image, continuously and adaptively altering both theadaptive template image and the PET or SPECT image by selecting anupdated control parameter to render the adaptive template image andupdated spatial transformation parameters to spatially normalize the PETor SPECT image for re-evaluation at (iv) within the single registrationprocedure and without performing the image registration until theadaptive template image converges to fit the PET or SPECT image; andwhen the adaptive template image converges to fit the PET or SPECTimage, register the PET or SPECT image in the reference space tocomplete the first registration.
 14. A positron emission tomography(PET) system comprising: an image processor for performing spatialnormalization and registration of a PET or a SPECT image, using a singleimage registration procedure, and for registering the PET the SPECTimage to an adaptive template image comprising a template image modelincluding a variability of values for one or more voxels in the adaptivetemplate image, wherein the variability of values for each voxel rangesfrom a value corresponding to a normal level of uptake of an imagingagent to an abnormal level of uptake of the imaging agent, and whereinthe imaging agent includes a dopamine transport agent DaTSCAN and alevel of uptake is determined for a striatum, the variability of valuesadjustable using a control parameter x, wherein the values of the voxelsare defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure,the image processor configured to: initiate a first registration of thePET or SPECT image into the reference space; spatially normalize the PETor SPECT image to the adaptive template image in the first registrationby: adjusting the template image model using a first control parameter,the adjusting to include adjusting among the variability of values forone or more voxels in the adaptive template image based on the firstcontrol parameter; rendering the adaptive template image from thetemplate image model; performing a spatial normalization operation byadjusting the PET or SPECT image using spatial transformationparameters; evaluating the adaptive template image and the PET or SPECTimaging to determine a convergence of the adaptive template image to thePET or SPECT image, wherein the convergence is determined by comparingvoxels of the adaptive template image to corresponding voxels of the PETor SPECT image, and wherein image registration between the adaptivetemplate image and the PET or SPECT image is not performed until theadaptive template image converges to fit the PET or SPECT image, andwhen the adaptive template image does not converge to fit the PET orSPECT image, continuously and adaptively altering both the adaptivetemplate image and the PET or SPECT image by selecting an updatedcontrol parameter to render the adaptive template image and updatedspatial transformation parameters to spatially normalize the PET orSPECT image for re-evaluation within the single registration procedureand without performing the image registration until the adaptivetemplate image converges to fit the PET or SPECT image; and when theadaptive template image converges to fit the PET or SPECT image,register the PET or SPECT image in the reference space to complete thefirst registration.
 15. A computer-implemented method of performingspatial normalization of a PET or a SPECT image, using a single imageregistration procedure, and registering of the PET or the SPECT image toan adaptive template image comprising a template image model including avariability of values for one or more voxels in the adaptive templateimage, wherein the variability of values for each voxel ranges from avalue corresponding to a normal level of uptake of an imaging agent toan abnormal level of uptake of the imaging agent, and wherein theimaging agent includes a dopamine transport agent DaTSCAN and a level ofuptake is determined for a striatum, the variability of valuesadjustable using a control parameter x, wherein the values of the voxelsare defined by an equation:I ^(i) _(template) =I ^(i) ₀ +I ^(i) _(slope) x, and wherein I^(i)_(template) is the value of the voxel i in the adaptive template image,I^(i) ₀ represents a value corresponding to a normal level of uptake ofthe imaging agent within that voxel, I^(i) _(slope) corresponds to amaximum additional amount of uptake of the imaging agent correspondingto a high level uptake of the imaging agent in that voxel with respectto the control parameter x, wherein the adaptive template image is in areference space and is used for registering the PET or the SPECT imagein the reference space during the single image registration procedure,the method comprising: initiating a first registration of the PET orSPECT image into the reference space; spatially normalizing the PET orSPECT image to the adaptive template image in the first registration by:adjusting the template image model using a first control parameter, theadjusting to include adjusting among the variability of values for oneor more voxels in the adaptive template image based on the first controlparameter; rendering the adaptive template image from the template imagemodel; performing a spatial normalization operation by adjusting the PETor SPECT image using spatial transformation parameters; evaluating theadaptive template image and the PET or SPECT imaging to determine aconvergence of the adaptive template image to the PET or SPECT image,wherein the convergence is determined by comparing voxels of theadaptive template image to corresponding voxels of the PET or SPECTimage, and wherein image registration between the adaptive templateimage and the PET or SPECT image is not performed until the adaptivetemplate image converges to fit the PET or SPECT image, and when theadaptive template image does not converge to fit the PET or SPECT image,continuously and adaptively altering both the adaptive template imageand the PET or SPECT image by selecting an updated control parameter torender the template adaptive image and updated spatial transformationparameters to spatially normalize the PET or SPECT image forre-evaluation within the single registration procedure and withoutperforming the image registration until the adaptive template imageconverges to fit the PET or SPECT image; and when the adaptive templateimage converges to fit the PET or SPECT image, registering the PET orSPECT image in the reference space to complete the first registration.